A Survey of Modeling and Application of Non-destructive and Destructive Degradation Tests

These days, most products are highly reliable which makes it very difficult or even impossible to obtain failure data on such products within a reasonable period of time prior to product release. Degradation tests are one way to overcome this obstacle by collecting degradation data (measurement of degradation) on such products. Based on different measurement processes, degradation tests can be divided into non-destructive and destructive degradation tests. In this chapter, we discuss a number of these two types of degradation models that have been developed in the literature to describe the degradation paths of products. In addition, some applications of degradation models of these two classes are also discussed.

[1]  Narayanaswamy Balakrishnan,et al.  Optimal Burn-In Policy for Highly Reliable Products Using Gamma Degradation Process , 2011, IEEE Transactions on Reliability.

[2]  Ye Zhang,et al.  Analysis of Destructive Degradation Tests for a Product With Random Degradation Initiation Time , 2015, IEEE Transactions on Reliability.

[3]  Nan Chen,et al.  Condition-based maintenance using the inverse Gaussian degradation model , 2015, Eur. J. Oper. Res..

[4]  Jen Tang,et al.  Determination of burn‐in parameters and residual life for highly reliable products , 2003 .

[5]  Enrico Zio,et al.  Component Importance Measures for Components With Multiple Dependent Competing Degradation Processes and Subject to Maintenance , 2016, IEEE Transactions on Reliability.

[6]  Shuen-Lin Jeng,et al.  Accelerated Discrete Degradation Models for Leakage Current of Ultra-Thin Gate Oxides , 2007, IEEE Transactions on Reliability.

[7]  Donghua Zhou,et al.  Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process , 2012, IEEE Transactions on Reliability.

[8]  B. Yum,et al.  Optimal design of accelerated degradation tests based on Wiener process models , 2011 .

[9]  Wayne Nelson,et al.  Analysis of Performance-Degradation Data from Accelerated Tests , 1981, IEEE Transactions on Reliability.

[10]  William Q. Meeker,et al.  Bayesian Methods for Accelerated Destructive Degradation Test Planning , 2012, IEEE Transactions on Reliability.

[11]  Hong-Fwu Yu,et al.  Designing an accelerated degradation experiment by optimizing the estimation of the percentile , 2003 .

[12]  Wenxing Zhou,et al.  Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines , 2013 .

[13]  Donghua Zhou,et al.  Multi-Sensor Information Based Remaining Useful Life Prediction With Anticipated Performance , 2013, IEEE Transactions on Reliability.

[14]  Xiaobing Ma,et al.  Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas , 2014 .

[15]  Tzong-Ru Tsai,et al.  Inference From Lumen Degradation Data Under Wiener Diffusion Process , 2012, IEEE Transactions on Reliability.

[16]  Weiwen Peng,et al.  Bivariate Analysis of Incomplete Degradation Observations Based on Inverse Gaussian Processes and Copulas , 2016, IEEE Transactions on Reliability.

[17]  Chih-Chun Tsai,et al.  Lifetime Inference for Highly Reliable Products Based on Skew-Normal Accelerated Destructive Degradation Test Model , 2015, IEEE Transactions on Reliability.

[18]  Narayanaswamy Balakrishnan,et al.  Optimal Design for Accelerated-Stress Acceptance Test Based on Wiener Process , 2015, IEEE Transactions on Reliability.

[19]  Wenbin Wang,et al.  A model for residual life prediction based on Brownian motion with an adaptive drift , 2011, Microelectron. Reliab..

[20]  Kwok-Leung Tsui,et al.  Degradation Data Analysis Using Wiener Processes With Measurement Errors , 2013, IEEE Transactions on Reliability.

[21]  Loon Ching Tang,et al.  Degradation-Based Burn-In Planning Under Competing Risks , 2012, Technometrics.

[22]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[23]  Weiwen Peng,et al.  Inverse Gaussian process models for degradation analysis: A Bayesian perspective , 2014, Reliab. Eng. Syst. Saf..

[24]  Fan Wu,et al.  A cost effective degradation-based maintenance strategy under imperfect repair , 2015, Reliab. Eng. Syst. Saf..

[25]  Gianpaolo Pulcini,et al.  A time-discrete extended gamma process for time-dependent degradation phenomena , 2012, Reliab. Eng. Syst. Saf..

[26]  William Q. Meeker,et al.  Accelerated Destructive Degradation Tests Robust to Distribution Misspecification , 2011, IEEE Transactions on Reliability.

[27]  Suk Joo Bae,et al.  Degradation Analysis of Nano-Contamination in Plasma Display Panels , 2008, IEEE Transactions on Reliability.

[28]  William Q. Meeker,et al.  Planning Accelerated Destructive Degradation Test with Competing Risks , 2010 .

[29]  Zhengqiang Pan,et al.  Optimal Design for Step-Stress Accelerated Test with Random Discrete Stress Elevating Times Based on Gamma Degradation Process , 2016, Qual. Reliab. Eng. Int..

[30]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[31]  Jyh-Jeu Horng Shiau,et al.  Analyzing accelerated degradation data by nonparametric regression , 1999 .

[32]  Christian Paroissin,et al.  Inference for the Wiener Process With Random Initiation Time , 2016, IEEE Transactions on Reliability.

[33]  Xiao Wang.  A PSEUDO-LIKELIHOOD ESTIMATION METHOD FOR NONHOMOGENEOUS GAMMA PROCESS MODEL WITH RANDOM EFFECTS , 2008 .

[34]  Loon Ching Tang,et al.  A Bayesian optimal design for accelerated degradation tests , 2010, Qual. Reliab. Eng. Int..

[35]  Weiwen Peng,et al.  A Bayesian optimal design for degradation tests based on the inverse Gaussian process , 2014 .

[36]  Enrico Zio,et al.  Semi-Markov Model for the Oxidation Degradation Mechanism in Gas Turbine Nozzles , 2016, IEEE Transactions on Reliability.

[37]  Francesco Penta,et al.  A gamma process model for the analysis of fatigue crack growth data , 2015 .

[38]  Nozer D. Singpurwalla.  Gamma Processes and their Generalizations: An Overview , 1997 .

[39]  Min Xie,et al.  Degradation-based burn-in with preventive maintenance , 2012, Eur. J. Oper. Res..

[40]  Sheng-Tsaing Tseng,et al.  Statistical Lifetime Inference With Skew-Wiener Linear Degradation Models , 2013, IEEE Transactions on Reliability.

[41]  Jun Yang,et al.  Measurement errors in degradation-based burn-in , 2016, Reliab. Eng. Syst. Saf..

[42]  Loon Ching Tang,et al.  Semiparametric Estimation of Gamma Processes for Deteriorating Products , 2014, Technometrics.

[43]  Jen Tang,et al.  Optimal burn-in time for highly reliable products , 2001 .

[44]  Min Xie,et al.  Classifying Weak, and Strong Components Using ROC Analysis With Application to Burn-In , 2007, IEEE Transactions on Reliability.

[45]  Chien-Tai Lin,et al.  Optimal Selection of the Most Reliable Design Based on Gamma Degradation Processes , 2014 .

[46]  Sheng-Tsaing Tseng,et al.  Optimum Allocation Rule for Accelerated Degradation Tests With a Class of Exponential-Dispersion Degradation Models , 2016, Technometrics.

[47]  X. Wang,et al.  Mis-specification Analyses of Nonlinear Wiener Process-based Degradation Models , 2016, Commun. Stat. Simul. Comput..

[48]  Narayanaswamy Balakrishnan,et al.  Accelerated Degradation Analysis for the Quality of a System Based on the Gamma Process , 2015, IEEE Transactions on Reliability.

[49]  Chanseok Park,et al.  New cumulative damage models for failure using stochastic processes as initial damage , 2005, IEEE Transactions on Reliability.

[50]  Sheng-Tsaing Tseng,et al.  Mis-Specification Analysis of Linear Degradation Models , 2009, IEEE Transactions on Reliability.

[51]  Tongmin Jiang,et al.  A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information , 2013, Reliab. Eng. Syst. Saf..

[52]  Inmaculada Torres Castro,et al.  A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes , 2015, Reliab. Eng. Syst. Saf..

[53]  Sheng-Tsaing Tseng,et al.  Optimal design for step-stress accelerated degradation tests , 2006, IEEE Trans. Reliab..

[54]  Jen Tang,et al.  Optimum step-stress accelerated degradation test for Wiener degradation process under constraints , 2015, Eur. J. Oper. Res..

[55]  Hong-Fwu Yu,et al.  A termination rule for degradation experiments , 1997 .

[56]  Rong Pan,et al.  A hierarchical modeling approach to accelerated degradation testing data analysis: A case study , 2011, Qual. Reliab. Eng. Int..

[57]  Sheng-Tsaing Tseng,et al.  Optimal burn-in policy by using an integrated Wiener process , 2004 .

[58]  Jen Tang,et al.  Estimating failure time distribution and its parameters based on intermediate data from a Wiener degradation model , 2008 .

[59]  L. Bordes,et al.  Parametric inference in a perturbed gamma degradation process , 2010, 1005.1214.

[60]  Xiaolin Wang,et al.  Real‐time Reliability Evaluation for an Individual Product Based on Change‐point Gamma and Wiener Process , 2014, Qual. Reliab. Eng. Int..

[61]  Hong-Fwu Yu,et al.  Designing an accelerated degradation experiment with a reciprocal Weibull degradation rate , 2006 .

[62]  Zhengqiang Pan,et al.  Multiple-Steps Step-Stress Accelerated Degradation Modeling Based on Wiener and Gamma Processes , 2010, Commun. Stat. Simul. Comput..

[63]  Hong-Fwu Yu,et al.  Designing a degradation experiment , 1999 .

[64]  Maurizio Guida,et al.  A random-effects model for long-term degradation analysis of solid oxide fuel cells , 2015, Reliab. Eng. Syst. Saf..

[65]  Min Xie,et al.  Optimal Burn-in Policy for Highly Reliable Products Using Inverse Gaussian Degradation Process , 2015 .

[66]  Suk Joo Bae,et al.  Cost-effective degradation test plan for a nonlinear random-coefficients model , 2013, Reliab. Eng. Syst. Saf..

[67]  Lirong Cui,et al.  Degradation Models With Wiener Diffusion Processes Under Calibrations , 2016, IEEE Transactions on Reliability.

[68]  Hong-Fwu Yu,et al.  An optimal designed degradation experiment for reliability improvement , 2002, IEEE Trans. Reliab..

[69]  M. Crowder,et al.  Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure , 2004, Lifetime data analysis.

[70]  Nan Chen,et al.  The Inverse Gaussian Process as a Degradation Model , 2014, Technometrics.

[71]  Christophe Bérenguer,et al.  An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks , 2015, Reliab. Eng. Syst. Saf..

[72]  Gianpaolo Pulcini.  A perturbed gamma process with statistically dependent measurement errors , 2016, Reliab. Eng. Syst. Saf..

[73]  Narayanaswamy Balakrishnan,et al.  Optimal Design for Degradation Tests Based on Gamma Processes With Random Effects , 2012, IEEE Transactions on Reliability.

[74]  Haitao Liao,et al.  Reliability inference for field conditions from accelerated degradation testing , 2006 .

[75]  Hao Qin,et al.  Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data , 2013 .

[76]  Suk Joo Bae,et al.  Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests , 2010, IEEE Transactions on Reliability.

[77]  Shing I. Chang,et al.  Optimal Two-Variable Accelerated Degradation Test Plan for Gamma Degradation Processes , 2016, IEEE Transactions on Reliability.

[78]  M. Tamborrino.  Approximation of the first passage time density of a Wiener process to an exponentially decaying boundary by two-piecewise linear threshold. Application to neuronal spiking activity. , 2015, Mathematical biosciences and engineering : MBE.

[79]  Tzong-Ru Tsai,et al.  SENSITIVITY ANALYSIS OF SAMPLE ALLOCATION AND MEASUREMENT FREQUENCY UNDER A DEGRADATION TEST WITH GAMMA PROCESS , 2015 .

[80]  Haowei Wang,et al.  Remaining Life Prediction Based on Wiener Processes with ADT Prior Information , 2016, Qual. Reliab. Eng. Int..

[81]  Narayanaswamy Balakrishnan,et al.  Optimal Design for Accelerated Destructive Degradation Tests , 2013 .

[82]  Chun Su,et al.  A Novel Multi-hidden Semi-Markov Model for Degradation State Identification and Remaining Useful Life Estimation , 2013, Qual. Reliab. Eng. Int..

[83]  Min Xie,et al.  Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance , 2015, Eur. J. Oper. Res..

[84]  Xiao Wang,et al.  An Inverse Gaussian Process Model for Degradation Data , 2010, Technometrics.

[85]  Shuo-Jye Wu,et al.  Optimal design of degradation tests in presence of cost constraint , 2002, Reliab. Eng. Syst. Saf..

[86]  Loon Ching Tang,et al.  Accelerated Degradation Test Planning Using the Inverse Gaussian Process , 2014, IEEE Transactions on Reliability.

[87]  Loon Ching Tang,et al.  A Distribution-Based Systems Reliability Model Under Extreme Shocks and Natural Degradation , 2011, IEEE Transactions on Reliability.

[88]  Narayanaswamy Balakrishnan,et al.  Optimal Step-Stress Accelerated Degradation Test Plan for Gamma Degradation Processes , 2009, IEEE Transactions on Reliability.

[89]  William Q. Meeker,et al.  Accelerated Destructive Degradation Test Planning , 2009, Technometrics.

[90]  Nozer D. Singpurwalla,et al.  Survival in Dynamic Environments , 1995 .

[91]  Chien-Yu Peng,et al.  Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data , 2015, Technometrics.